(iv) Low Deployment Cost: While graphics processing units (GPUs) have revolutionized deep-learning [1]}, GPUs are energy-hungry and expensive to maintain continually for inference using small models, leading to long term financial and environmental degeneration [2]}. A Cortex M4 class microcontroller costs around 5-10 USD and can run on a coin-cell battery for months, if not years [3]}. TinyML allows these microcontrollers to be exploited for ultra-low-power and low-cost AI inference.
